## ---- echo = TRUE,eval = FALSE------------------------------------------- # path_lip<-getKEGGdata(KEGG_path="Lip_met") ## ---- eval = FALSE, echo = FALSE----------------------------------------- # knitr::kable(colnames(path_lip), digits = 2, # caption = "List of pathways in lipid metabolism",row.names = FALSE) ## ---- echo = TRUE,eval = FALSE------------------------------------------- # pathcell_grow_d<-getKEGGdata(KEGG_path="cell_grow_d") ## ---- eval = FALSE, echo = FALSE----------------------------------------- # knitr::kable(colnames(pathcell_grow_d), digits = 2, # caption = "List of pathways in cellular processes",row.names = FALSE) ## ---- eval = FALSE------------------------------------------------------- # score_euc_dist_Lip_met<-dev_std_crtlk(dataFilt=Data_CANCER_normUQ_filt,path_lip) ## ---- eval = FALSE------------------------------------------------------- # tumo<-SelectedSample(Dataset=Data_CANCER_normUQ_filt,typesample="tumor")[,1:100] # norm<-SelectedSample(Dataset=Data_CANCER_normUQ_filt,typesample="normal")[,1:100] # nf <- 60 # res_class<-svm_classification(TCGA_matrix=score_euc_dist_Lip_met,nfs=nf, # normal=colnames(norm),tumour=colnames(tumo)) ## ---- eval = FALSE------------------------------------------------------- # better_perf<-select_class(auc.df=res_class,cutoff=0.80) ## ---- eval = FALSE------------------------------------------------------- # matrix_best_perf<-process_matrix(measure=score_euc_dist_Lip_met,list_perf=better_perf) # tumo_bestlipd<-SelectedSample(Dataset=matrix_best_perf,typesample="tumor")[,1:100] # score_bestlipd<-colMeans(tumo_bestlipd) ## ---- eval = FALSE------------------------------------------------------- # tumo_cell_grow_d<-SelectedSample(Dataset=Data_CANCER_normUQ_filt,typesample="tumor")[,1:100] # score_euc_dist_cell_grow_d<-dev_std_crtlk(dataFilt=tumo_cell_grow_d,pathcell_grow_d) ## ---- eval = FALSE------------------------------------------------------- # score__cell_grow_d<-process_matrix_cell_process(score_euc_dist_cell_grow_d) # score__cell_grow_d_mean<-colMeans(score__cell_grow_d) ## ---- eval = FALSE------------------------------------------------------- # correlazione<-cor(score__cell_grow_d_mean,score_bestlipd) # plot_matrix<-cbind(score__cell_grow_d_mean,score_bestlipd) #